No abstract is provided for this article.
No abstract is provided for this article.
Enzyme reactions with inhibition and cooperativity are modelled in terms of a pair of coupled nonlinear reaction–diffusion equations. The governing equations are solved using stochastic cellular automata with local rules derived from the corresponding nonlinear partial differential equations. The parallel cellular automaton is implemented using domain decomposition according to the nature of the locality of its update rules. Numerical simulations show stable 2-D and 3-D pattern formation, and complex patterns have the interesting feature of self-organized criticality. The numerical results of cellular automata are also compared with results obtained from finite difference and finite element methods.
All design problems in telecommunications can be formulated as optimization problems, and thus may be tackled by some optimization techniques. However, these problems can be extremely challenging due to the stringent time requirements, complex constraints, and a high number of design parameters. Solution methods tend to use conventional methods such as Lagrangian duality and fractional programming in combination with numerical solvers, while new trends tend to use evolutionary algorithms and swarm intelligence. This chapter provides a summary review of the bio-inspired optimization algorithms and their applications in telecommunications. We also discuss key issues in optimization and some active topics for further research.
No abstract is provided for this article.
The effectiveness of enterprise human resource management is an important topic of human resource management in academic studies, and it is the key for enterprises to achieve long-term sustainable development. Up to now, fruitful research achievements have been achieved, but there is still a lack of a systematic literature review. Based on 218 research results collected in the CNKI and SSCI databases of the WOS from 1975 to 2021, this paper systematically reviews the enterprise human resource management effectiveness from the aspects of evolution, topic overview, and content sorting by using bibliometrics with Citespace software and a systematic literature review. This paper expounds the concept of enterprise human resource management effectiveness, analyzes the existing research results, and finds out where the research gap is. Based on this, this paper puts forward the prospect of future research topics from the aspects of measurement methods, influencing factors, and results, which not only helps to systematically reveal the overview of the existing research in the field of enterprise human resource management effectiveness, but also provides direction for further research.
A multispecies artificial ecosystem is formulated using cellular automata with species interactions and food chain hierarchy. The constructed finite state automaton can simulate the complexity and self-organized characteristics of the evolving multispecies living ecosystems. Simulations show that a small perturbation or extinction event may affect many other species in the ecosystem in an avalanche manner. Both the avalanches and the extinction arising from these changes follow a power law, reflecting that the multispecies living ecosytems have the characteristics of self-organized criticality.
In online display advertising, guaranteed contracts and real-time bidding (RTB) are two major ways to sell impressions for a publisher. For large publishers, simultaneously selling impressions through both guaranteed contracts and in-house RTB has become a popular choice. Generally speaking, a publisher needs to derive an impression allocation strategy between guaranteed contracts and RTB to maximize its overall outcome (e.g., revenue and/or impression quality). However, deriving the optimal strategy is not a trivial task, e.g., the strategy should encourage incentive compatibility in RTB and tackle common challenges in real-world applications such as unstable traffic patterns (e.g., impression volume and bid landscape changing). In this paper, we formulate impression allocation as an auction problem where each guaranteed contract submits virtual bids for individual impressions. With this formulation, we derive the optimal bidding functions for the guaranteed contracts, which result in the optimal impression allocation. In order to address the unstable traffic pattern challenge and achieve the optimal overall outcome, we propose a multi-agent reinforcement learning method to adjust the bids from each guaranteed contract, which is simple, converging efficiently and scalable. The experiments conducted on real-world datasets demonstrate the effectiveness of our method.
Catalytic reactions with inhibition and cooperativity are common in chemical engineering, processing of engineering materials and biological systems. A theoretical model for competitive and cooperative activity in enzyme reactions is formulated in terms of a pair of coupled nonlinear reaction–diffusion equations. A new stochastic cellular automaton is then constructed to simulate this nonlinear system. Numerical simulations show stable two- and three-dimensional Turing pattern formation, and complex patterns have the interesting feature of self-organized criticality.
The popularization of bridge employment is conducive to the realization of active aging, which requires not only the subjective initiative of retirees but also the active cooperation of local governments and relevant enterprises. This study combined evolutionary game theory with system dynamics to model and simulate the behavior of local government and enterprises on bridge employment, aiming to highlight the importance of government behavior and enterprise actions in the process of active aging and to analyze the effectiveness of different incentive and punishment mechanisms in promoting bridge employment. Results show that the system with dynamic incentive and punishment is easier to keep stable than the system with static incentive and punishment; specifically, the dynamic mechanism with low incentive intensity and high punishment intensity is better than other mechanisms. Finally, this study emphasized the importance of government policy to bridge employment and put forward relevant management implications.
In this article, we present nature-inspired techniques for automatic image registration of multi-temporal satellite images. Multi-temporal satellite image registration is becoming increasingly important to aid in flood damage assessment. We consider two images in the registration process: one before-flood image and another during-flood image. The objective is to maximise the similarity metric (of these two images) using information theoretic measures such as mutual information (MI). The maximum MI would imply that the images are better registered. The function of these metrics for transformation parameters is generally non-convex and irregular and, therefore, makes it difficult to use standard optimisation methods for the global solution. In this study, nature-inspired techniques – genetic algorithm (GA), particle swarm optimisation (PSO) and firefly algorithm (FA) are used to search for the maximum MI. The multi-temporal images – Linear Imaging Self-Scanning Sensor III (LISS III) image (before flood) and...
No abstract is provided for this article.
Nature-inspired algorithms such as Particle Swarm Optimization and Firefly Algorithm are among the most powerful algorithms for optimization. In this paper, we intend to formulate a new metaheuristic algorithm by combining Levy flights with the search strategy via the Firefly Algorithm. Numerical studies and results suggest that the proposed Levy-flight firefly algorithm is superior to existing metaheuristic algorithms. Finally implications for further research and wider applications will be discussed.
Proper heat transfer management is important to key electronic components in microelectronic applications. Pulsating heat pipes (PHP) can be an efficient solution to such heat transfer problems. However, mathematical modelling of a PHP system is still very challenging, due to the complexity and multiphysics nature of the system. In this work, we present a simplified, two-phase heat transfer model, and our analysis shows that it can make good predictions about startup characteristics. Furthermore, by considering parameter estimation as a nonlinear constrained optimization problem, we have used the firefly algorithm to find parameter estimates efficiently. We have also demonstrated that it is possible to obtain good estimates of key parameters using very limited experimental data.
Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario.
No abstract is provided for this article.
